Keynote Speech

Localization of Brain Activations Based on EEG Recordings and Sparse Signal Recovery Theory

  Sparse signal recovery is often formulated as an l1-norm minimization problem. However, unless certain conditions are satisfied, there is no guarantee that the least l1-norm solution will also be a sparsest solution. In this talk, we show that by appropriately weighting the sensing matrix, we can formulate an l1-norm minimization problem whose solution is guaranteed to be one of the sparsest solutions. The weights can be obtained based on a low resolution estimate of the sparse signal, obtained for example via a method that does not encourage sparsity.
  The proposed weighting approach is a good candidate for Electroencephalography (EEG) sparse source localization, where measurements of sensors, placed on a subject’s head are used to localize activations inside the brain. In many cases, the locations of these activations are related to the subject’s reactions or intensions, and estimating them via a non-invasive and inexpensive modality like EEG can find applications in several domains, including cognitive and clinical neuroscience as well as brain-computer interfaces (BCIs).  In response to simple tasks, the brain activations are sparse, and thus, their localization based on the EEG recordings can be formulated as a sparse signal recovery problem. In this case, the corresponding basis matrix, referred to as lead field matrix, has high mutual coherence, which means that the least l1-norm solution will not necessarily lead to the brain sources. In spite of the high coherence of the lead field matrix, the proposed weighting approach can still estimate the sources inside the brain. In this talk, this is demonstrated by localizing active sources in the brain corresponding to an auditory task from EEG recordings of human subjects.  

  Athina P. Petropulu received her undergraduate degree from the National Technical University of Athens, Greece, and the M.Sc. and Ph.D. degrees from Northeastern University, Boston MA, all in Electrical and Computer Engineering. She is Distinguished Professor at the Electrical and Computer Engineering (ECE) Department at Rutgers, having served as chair of the department during 2010-2016.  Before joining Rutgers in 2010, she was faculty at Drexel University. She held Visiting Scholar appointments at SUPELEC, Universite’ Paris Sud, Princeton University and University of Southern California. Dr. Petropulu's research interests span the area of statistical signal processing, wireless communications, signal processing in networking, physical layer security, and radar signal processing. Her research has been funded by various government industry sponsors including the National Science Foundation (NSF), the Office of Naval research, the US Army, the National Institute of Health, the Whitaker Foundation, Lockheed Martin and Raytheon.
   Dr. Petropulu is Fellow of IEEE and recipient of the 1995 Presidential Faculty Fellow Award given by NSF and the White House. She has served as Editor-in-Chief of the IEEE Transactions on Signal Processing (2009-2011), IEEE Signal Processing Society Vice President-Conferences (2006-2008), and is currently member-at-large of the IEEE Signal Processing Board of Governors. She was the General Chair of the 2005 International Conference on Acoustics Speech and Signal Processing (ICASSP-05), Philadelphia PA, and is General Co-Chair of the 2018 IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), to be held in Kalamata Greece in June 2018. She is recipient of the 2005 IEEE Signal Processing Magazine Best Paper Award, and the 2012 IEEE Signal Processing Society Meritorious Service Award for "exemplary service in technical leadership capacities". She is IEEE Distinguished Lecturer for the Signal Processing Society for 2017-2018. More info on her work can be found at www.ece.rutgers.edu/~cspl

Noise reduction and modulation enhancement schemes in optical data storages and photonic devices

  In the 4th industrial revolution era, the storage data access speed became more and more important due to the heavy utilization of data over the networks. In this talk, the figure of merit of the access network devices will be discussed followed by current schemes of noise reduction and modulation enhancement regarding related photonic devices. Specifically, the external feedback effects on the relative intensity noise characteristics of laser diode will be discussed taking into account the spontaneous emission noise and the high frequency modulation of the injected current. A Langevin diffusion model was exploited to characterize its relative intensity noise, and the spontaneous emission noise components were quantitatively evaluated from the optical gain properties of the active regions. 
  Secondly, a modulation efficiency enhancement scheme by using the external modulators will be discussed. Especially, the waveguide-coupled micro-ring cavity resonator utilizing the self-aligned total internal reflector mirrors will be discussed based on numerical and experimental results. Unlike the conventional electro absorption modulators, the micro-ring cavity resonator was found to exhibit a singular modulation characteristics depending on the coupling coefficient between the micro-ring cavity and the input/output waveguide, which can be exploited to enhance the optical modulation efficiency as well as the extinction ratio.
  Finally, the ultimate wavelength stabilization schemes will be discussed by using the three dimensional quantum confinement structures fabricated by the edge-defined nanowires. Their modulation bandwidth for various materials and structures will be outlined considering current processing limitations.

  Dr. Jong Chang Yi received his B. E. in electronic engineering from Seoul National University in 1983, M. Sc in electrical and electronic engineering from Korea Advanced Institute of Science and Technology (KAIST) in 1985, and Ph. D in electrical and computer engineering from University of California, Santa Barbara (UCSB) in 1994. He worked at Korea Institute of Science and Technology (KIST), Seoul, Korea, as a researcher on photonic devices between 1985 and 1989. He also worked at the UCSB research center on Quantized Electronic Structure as a graduate researcher between 1989 and 1994. Since 1994, he has been at Hongik University, Seoul, Korea, and there he had served as the head of the School of Electronic and Electrical Engineering and directors of the Metamaterials Electronic Devices Research Center and the Wired and Wireless Optical Communications Research Center which are government-funded research centers at Hongik University. Currently, He serves as IEEE Seoul Section Chair, a board member of the Optical Society of Korea, and a board member of LUMENS, Inc. Dr. Jong Chang Yi’ researches focus on high speed low noise photonic switches, high efficiency multi-junction solar cells, high brightness LEDs, high sensitivity graphene devices for sensors, and holographic optical elements for display and wearable devices. His previous researches have been published in more than 70 papers including a cover of Science in 1991 and 10 international patents.

Automated medical image classification: Techniques and Challenges

  In this talk, I will first give an overview of automated biomedical image classification. I will then present in some details two of our recent medical image classification methods: One is based on a cascade of an SVM with a reject option and subspace analysis and the other is based on deep learning. The first method consists of two stages and it exploits biomedical images with different geometric correspondence. The second method proposes an improved deep convolutional neural network (CNN)  by exploiting transfer learning and feature concatenation. Both classification methods work well for some real-world noisy biomedical images. Finally, some related challenges in automated biomedical image classification will be discussed.

  Zhiping Lin received the B.Eng. degree in control engineering from South China Institute of Technology, Canton, China in 1982 and the Ph.D. degree in information engineering from the University of Cambridge, England in 1987. He was with the University of Calgary, Canada for 1987-1988, with Shantou University, China for 1988-1993, and with DSO National Laboratories, Singapore for 1993-1999. Since 1999, he has been with Nanyang Technological University (NTU), Singapore. He is a Program Director at Centre for Bio Devices and Signal Analysis, NTU. Dr. Lin was the Editor-in-Chief of Multidimensional Systems and Signal Processing for 2011 – 2015, and has being in its editorial board since 1993. He was an Associate Editor of Circuits, Systems and Signal Processing for 2000-2007 and an Associate Editor of IEEE Transactions on Circuits and Systems - II for 2010-2011. He was a reviewer for Mathematical Reviews for 2011-2013. He is currently a Subject Editor of the Journal of the Franklin Institute and served as a Guest Editor for a recent special issue on “Advances in Machine Learning for Signal Analysis and Processing” for the same journal. He was General Chair of the Ninth International Conference on Information, Communications and Signal Processing (ICICS) in 2013 and also served for many other international conferences. His research interests include multidimensional systems and signal processing, statistical and biomedical signal processing, and machine learning. He is the co-author of the 2007 Young Author Best Paper Award from the IEEE Signal Processing Society, Distinguished Lecturer of the IEEE Circuits and Systems Society for 2007-2008, and received the Best Paper Awards at ELM 2015 and ELM 2017.

SLAM Technology Based on Fusion of 3D Data Flow

  Simultaneous Localization and Mapping (SLAM) is a very helpful technology for 3D reconstruction and machine navigation in computer vision, robotics and virtual reality. In recent years, great progress in mobile 3D sensor development has opened new opportunities for the applications of the technology in industry and other daily-life situations. But nevertheless, we still face big challenges when we try to use the techniques in complex un-structured environments or outdoor spaces where sensors embedded there in advance are not available. In the talk, I will introduce some new SLAM approaches by making good use of 3D data flow and high-level scene structures. Main topics include: (1) a probabilistic and adaptive framework for 3D data fusion with sensor error prior; (2) edge enhanced visual odometry for robots navigating in indoor or outdoor spaces.

  Hongbin Zha received PhD degree in electrical engineering from Kyushu University, Japan, in 1990. After working as an associate professor in Kyushu University, he has been a professor at the Key Laboratory of Machine Perception (MOE), Peking University, since 2000. His research interests include computer vision, robotics, and human-machine interactions. He has published more than 300 technical papers. He received the Franklin V. Taylor Award from the IEEE Systems, Man, and Cybernetics Society in 1999, and Best Paper Awards in Euromed 2010 and CCCV 2017.

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Important Date
  • May. 20, 2018 : Extended Submission of Papers
  • June. 20, 2018 : Notification of Acceptance
  • July. 10, 2018 : Submission of Final Papers
  • July. 10, 2018 : Pre-registration
  • Aug. 12-16, 2018 : Conference
Contact Us
  • Ms. Xiaofang TANG;
  • Dr. Gaoyun AN
  • IIS Beijing Jiaotong University
  • Beijing, China, 100044
  • Email: bfxxstxf@bjtu.edu.cn
  • gyan@bjtu.edu.cn